Senior Machine Learning Test Engineer (United Kingdom)

Join Autodesk as a Senior Machine Learning Test Engineer in the Research Enablement team. You will work closely with researchers, Machine Learning Engineers, and software engineers to define and uphold quality standards for ML systems. Your responsibilities will include defining ML quality strategy, designing and maintaining model evaluation suites, testing ML models from the product side, automating ML QA workflows, and mentoring teams on ML QA best practices. You should have a strong background in software engineering, experience in test automation, and a passion for reliable evaluation of ML models and data.

• Definir la estrategia de calidad de ML y los criterios de aceptación en todos los niveles.
• Diseñar y mantener suites de evaluación de modelos, métricas y conjuntos de datos de prueba.
• Automatizar los flujos de trabajo de QA de ML utilizando Python y CI/CD (por ejemplo, GitHub Actions, Jenkins).
• Understanding of software architecture and design patterns
• Excellent problem-solving skills and attention to detail
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• Strong programming skills in Python, with experience in test automation
• Experience designing QA frameworks or platforms used by multiple teams
• 7+ years of professional experience in software engineering or QA for ML/AI systems
• Familiarity with popular CAD environments tooling
• Proficient in Automation and UAT test suite/framework
• Strong communication and collaboration skills
• Ability to work in an agile development environment
• You are comfortable building scalable and maintainable systems that will be relied on by others
• You are comfortable working in newly forming ambiguous areas
• You can communicate well with others
• You enjoy learning and collaborating across global locations
• Comfortable building QA systems from scratch and writing maintainable automation
• You demonstrate initiative to provide solutions and to learn and develop new technologies
• Experience testing ML services in production environments
• Knowledge of experiment tracking tools (e.g., Comet, MLflow, Weights & Biases)
• Passion for learning new technologies and improving existing systems
• Experience with cloud providers (e.g., AWS, Azure, Google Cloud Platform)
• Experience with ML evaluation methods, metrics, and benchmarking
• Familiarity with MLOps practices (model monitoring, drift, deployment checks)
• Experience with data pipelines and orchestration tools (e.g., Airflow, Metaflow)
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow)
• Experience with CI/CD tools and processes
• Experience with data validation tooling (e.g., Great Expectations) or labeling workflows